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arXiv Computation and Language By Chengxi Li, Shaoxuan Li, Deyao Hong, Qingle Liu, Yi Huang, Puheng Bian, Minzhi Xu, Lanyue Zhang, Mingze Sun, Xin Li, Calvin Xiao, Shengyuan Wang, Huan-ang Gao, Jiazheng Xu, Kaisen Yang, Hongning Wang

DiabAgent: Toward Agentic Physiological Control

Read the original on arXiv Computation and Language →

DiabAgent introduces a research suite for agentic physiological control in type 1 diabetes, comprising the APC framework, DiabetSim simulator, and SweetBench benchmark. The APC framework uses medical-device tools and ongoing observations to infer patient states, accumulate experience, and select treatment actions. In virtual testing with DiabetSim, APC outperforms the open-source baseline OpenAPS, achieving a COGI of 73.5 versus 71.2, and ablation studies show that individual adaptation and contextual information improve glucose control.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computation and Language.

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